ending extreme poverty: progress, but uneven and …...scenarios about global poverty in 2030.3 to...

1

Chapter 1 presents the latest data on global and regional extreme poverty rates using the inter-

national poverty line of US$1.90 in 2011 purchasing power parity dollars. The chapter discusses

the trends, the geographical concentration, and the profile of extreme poverty. It also reflects

on data coverage and methodological issues and their consequences on global estimates.

Extreme poverty declined to 10 percent of the world’s population in 2015, meaning 1

person in every 10 in the world was living in extreme poverty. This rate dropped from nearly

36 percent in 1990, resulting in a world with more than a billion fewer people living in extreme

poverty. Although this progress is remarkable, 10 percent equates to 736 million people still

living in extreme poverty in 2015, and there is evidence that the pace of poverty reduction is

starting to decelerate. There remain significant challenges to reaching the goal of a world free

of poverty. Meeting the global target of reducing extreme poverty to less than 3 percent will

require substantially greater efforts.

Ending Extreme Poverty: Progress, but Uneven

and Slowing

19

Monitoring extreme poverty: A quarter century of progressThe World Bank is committed to eradicating poverty. The twin goals of ending extreme poverty and promoting shared prosperity in a sustainable manner accord well with the post-2015 development agenda and the Sustain-able Development Goals (SDGs) to ensure that all people can fulfill their potential in dignity and equality and in a healthy environ-ment (box 1.1). Monitoring global poverty is critical for tracking progress and identifying areas that require additional policy actions.

In 2015, an estimated 736 million people were living below the international poverty line (IPL), currently set at US$1.90 in 2011 purchasing power parity (PPP) dollars. The total count was down from 1.9 billion peo-ple in 1990. Despite the world population in-creasing by more than 2 billion people over this period, more than a billion fewer people

lived in poverty in 2015 than in 1990. Not only are there now fewer poor people but, on average, the poor are also now less poor. In 1990, the average shortfall between what the poor consumed and the IPL was 35 per-cent (of the IPL). This shortfall shrank to an average of 31 percent in 2015. The total con-sumption shortfall of the poor (the sum of all consumption shortfalls of the poor) in 2015 had shrunk to about one-third of its size from 1990. (For more details on the con-sumption shortfall of the poor, and the depth and severity of poverty, see annex 1A.) De-spite this impressive progress in terms of the declining poverty rate, the number of poor, and the consumption shortfall of the poor, the number of people living in extreme pov-erty globally remains unacceptably high.

The World Bank has set a specific target to help guide the work in eradicating poverty: reduce the global share of people living in extreme poverty to less than 3 percent. Over

20 POVERTY AND SHARED PROSPERITY 2018

BOX 1.1 Alignment of the SDGs and the Twin Goals of the World Bank Group

On April 20, 2013, the Board of Executive Directors of the World Bank adopted two ambitious goals: ending extreme poverty globally and promoting shared prosperity in every country in a sustainable way. Progress toward the first of these goals is measured by monitoring the share of the global population living below the international poverty line. The World Bank set a target of reducing extreme poverty to less than 3 percent by 2030 and to ensure continued focus and steady progress toward the goal, the institution set an interim target of 9 percent by 2020.

The second goal is not defined globally, but rather tracks progress at the country level. Progress on the shared prosperity goal is measured by the growth in the average consumption or income expenditure of the poorest 40 percent of the population (the bottom 40) in a country. This goal is not associated with a target in 2030, but it reflects the aim that every country should promote the welfare of its least privileged citizens for a more inclusive and equitable society.

On September 25, 2015, the United Nations General Assembly adopted the 17 Sustainable Development Goals (SDGs) and 169 targets as part of the 2030 Agenda for Sustainable Development,

building on the Millenium Development Goals (MDGs). Ending poverty in all its forms and dimensions is the first of the 17 SDGs. The General Assembly Resolution recognizes that eradicating poverty is the greatest global challenge and an indispensable requirement for sustainable development.

The SDGs and the World Bank’s twin goals are aligned. The goals of ending extreme poverty within a generation and promoting shared prosperity in a sustainable manner accord with the 2030 Agenda for Sustainable Development to ensure that all human beings can fulfill their potential in dignity and equality and in a healthy environment. In contrast to the SDGs, the World Bank’s twin goals do not set distinct country-specific targets or targets for the multiple dimensions of poverty, equity, and sustainability. However, the World Bank recognizes that poverty is multidimensional, and sustainability is critical. The pursuit of these goals will require the concerted effort of all stakeholders. Over the years, the World Bank has collaborated with the United Nations in nearly every region and sector, and its engagement has deepened since the adoption of the MDGs, and now with the SDGs.

the last decades, remarkable progress has been made in reducing extreme poverty (figure 1.1; see box 1.2 for details on the data used). The world attained the first Millen-nium Development Goal—to cut the 1990 poverty rate in half by 2015—six years ahead of schedule. With continued reductions, the global poverty rate—the share of the world’s population living below the IPL—dropped from about 36 percent in 1990 to 10 per-cent in 2015, that is, more than a 70 percent reduction.

Over the 25 years from 1990 to 2015, the global poverty rate fell by slightly more than 25 percentage points, or an average decline of 1 percentage point a year. (Gauged according to today’s population, 1 percent equates to about 76 million people.) Given this trend of

steady poverty reduction, the world is clearly on track to reach the interim poverty target of 9 percent by 2020 set by the World Bank to monitor progress toward the 2030 goal.1 Forecasts for 2018 indicate that this target has already been surpassed.

Reducing poverty to 3 percent by 2030 from 10 percent in 2015 will require an additional 7-percentage-point reduction in the poverty rate in 15 years. If, over the last 25 years, poverty has steadily declined at 1 per-centage point a year, it would seem reasonable to assume that the world is well on track to re-ducing poverty by at least 7 percentage points over the next 15 years. The rate of poverty re-duction could be cut in half to a 1-percentage- point decline every two years, and the world would still reach the 3 percent target.

http://www.unmillenniumproject.org/goals/

https://sustainabledevelopment.un.org/sdgs

ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 21

BOX 1.2 Data Overview

Data source

The data for this chapter come from PovcalNet, which is an online analysis tool for global poverty monitoring hosted by the World Bank (http://iresearch.worldbank.org/PovcalNet). PovcalNet was developed with the purpose of public replication of the World Bank’s poverty measures for the IPL. PovcalNet contains poverty estimates from more than 1,600 household surveys spanning 164 countries.a Most of the surveys in PovcalNet are harmonized through the Global Monitoring Database, the World Bank’s repository of household surveys.

Derivation of country-level estimates

The national poverty rates from household surveys are based on measures of household consumption or income. In the current 2015 estimates, about 40 percent of the countries covered use income, but the use of income rather

than consumption has been increasing over time. The differences between income and consumption measures matter for comparing trends and patterns in poverty. To assure that the income and consumption levels from different countries are comparable, they need to be expressed in the same unit. To this end, consumer price indexes and purchasing power parities are applied. Because the frequency and timing of household surveys vary across countries, comparable country-level estimates require projecting the survey data to the reference year for which global poverty is expressed, here 2015. When the timing of surveys does not align with the reference year, PovcalNet “lines up” the survey estimates to the reference year.

Derivation of regional/global estimates

To arrive at a regional and global estimate of poverty, population-weighted

(continued)

FIGURE 1.1 Global Poverty Rate and Number of Poor, 1990–2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/.Note: PPP = purchasing power parity.

35.9

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http://iresearch.worldbank.org/PovcalNet/



erty reported for 2015? The global poverty es-timates are based on household surveys from 164 countries, and these surveys are carried out independently, typically by national sta-tistical offices or national planning ministries. The surveys are complex and lengthy, requir-ing significant amounts of labor and time to be implemented effectively; and, in most countries, they are not carried out every year. Countries implement household surveys that measure poverty status once every three to five years (Serajuddin et al. 2015). It also takes time to gather, process, and analyze these data. There is thus frequently a lag between the completion of the survey fieldwork and the publication of the data for the global pov-erty counts (Independent Evaluation Group 2015). For these reasons, 2015 is the most re-cent year for which there are sufficient data to estimate a global poverty rate.2 (For details on how data are shifted forward and backward in time to produce the 2015 estimate, see appen-dix A at the end of the report.)

However, if assumptions are made about the relationship between economic growth as observed in national accounts (such as the real growth in gross domestic product [GDP]) and in surveys, as well as on popula-tion projections, it is possible to nowcast the global poverty rate in 2018 and also generate scenarios about global poverty in 2030.3 To nowcast poverty in 2018, it is assumed that

Despite this optimistic portrait of the path toward the target, there are reasons for concern. One reason is the existence of some evidence that the rate of poverty reduction has recently slowed. Between 2011 and 2013, poverty declined by 2.5 percentage points, but, over the two years between 2013 and 2015, it declined by only 1.2 points. Although this apparent change in the rate of poverty reduction over these two years should be in-terpreted with caution because of data chal-lenges, it is a first potential signal of change.

To assess whether this recent change in the path of poverty reduction is an aberration or a warning sign of what the future holds, forecasts of how poverty may evolve up to 2030 can be very informative. Such forecasts should be viewed with caution though, be-cause the factors that affect global poverty reduction are complex, and because what the future holds is unknown. For example, economic growth is a key factor in reducing poverty, but it can be volatile and difficult to predict. Nonetheless, without forecasts, it is not possible to clarify whether the current trajectory is adequate to reach the target.

Nowcasts and forecasts to 2030

The current estimate of the global poverty rate—10 percent—refers to 2015, which is three years out of date. Why in 2018 is pov-

BOX 1.2 Data Overview (continued)

average poverty rates are calculated for each region.b Some countries have no household survey data to monitor poverty. No direct value is imputed for these countries; rather it is assumed that the average for the region based on the countries with data available is the same as the regional average for all countries. The number of poor in each region is the product of the region’s poverty rate and the total regional population. The global

estimate of the number of poor is the product of the population-weighted mean of the regional poverty rates and the total world population.

Further information

For further information regarding the data sources, geographical regions, data issues, and assumptions underlying the global, regional, and country-level estimates, see appendix A at the end of the report.

a. The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics.b. Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models (Source: World Development Indicators).


that there seems to be a significant slowdown in the rate of global poverty reduction. From 2013 to 2015, poverty declined by 0.6 per-centage points per year; this is slower than the 25-year average decline of a percentage point per year. Between 2015 and 2018 the nowcast suggests that the rate of poverty re-duction has further slowed to less than half a point per year.

Projecting global poverty to 2030 is more challenging, but it is possible to consider how global poverty may evolve under different scenarios. Four scenarios are considered as described below. The first scenario assumes that every country grows at its average growth rate from 2005–15. This growth rate is then used to “grow” the household survey mean over time, in a way that does not change the level of inequality. This approach makes it pos-sible to move the entire distribution of con-sumption or income forward in time, starting with the 2018 nowcast and moving up to 2030.

The second scenario is like the first, ex-cept for one difference: the growth rate for each country is not its historical average, but rather the historical average for its region.

each household’s welfare grows at a fraction of the growth in GDP per capita. Only a frac-tion of the growth in GDP per capita is passed through to the welfare vector because there is a historical divergence between growth in consumption or income observed in sur-veys and the growth observed in national ac-counts. The fraction that is passed through to the welfare vector is based on examining past data on the average relationship between sur-vey means and national accounts data (Prydz, Jolliffe, and Serajuddin, forthcoming).4 With this approach, it is assumed that the scaled growth accrued equally (in proportionate terms) to everyone in a country regardless of individual income level. If inequality changed from 2015 to 2018, this assumption will not hold, and poverty will be higher or lower de-pending on the change in inequality (World Bank 2016b; Lakner, Negre, and Prydz 2014).

Under these assumptions, the 2018 nowcast for the global poverty rate is 8.6 per-cent (figure 1.2). This means that the 2020 in-terim target has likely already been achieved. One important implication of this estimate is that it provides another piece of evidence

FIGURE 1.2 Global Poverty Rate Projections to 2030

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/; World Development Indicators; World Economic Outlook; Global Economic Prospects; Economist Intelligence Unit. Note: The 2018 nowcast uses realized and projected growth in GDP per capita and household final consumption expenditure per capita from 2015 to 2018 to grow the 2015 welfare vector. “Historical country (regional) growth” assumes that the annual growth rates countries (regions) experienced from 2005 to 2015 continue from 2018 to 2030. “6% annual growth + 2 pp premium” assumes that all countries grow by 6 percent annually from 2018 to 2030, and that the bottom 40 percent on average grow with an additional 2 percentage points (pp). All assumed growth rates are real, per capita growth.

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10-year historic average growth rate (based on growth from 2000 to 2010) was almost 4 percent, but this was sustained for only a few years and has since declined slightly.

How can it be that poverty has declined by 25 percentage points over the last 25 years, yet the only forecasts that suggest poverty will be reduced by 7 percentage points over the next 15 years are based on unprecedented growth patterns and rates?

Uneven progress: A regional profile of poverty reduction

There are several parts to the answer of this question and many of them hinge on the gen-eral idea that progress has been uneven, which is linked to the theme of this report. A slightly more specific answer to the question posed above is that not all regions have shared in the benefits of the global reduction in poverty.

To better understand why the simulations forecast a challenging path for reaching the target, it is useful to examine the changing regional profile of poverty that has been brought about by the differing rates of pov-erty reduction. Between 1990 and 2015, the regional profile of poverty has changed sig-nificantly. In 2015, more than half of the global poor resided in Sub-Saharan Africa and more than 85 percent of the poor re-sided in either Sub-Saharan Africa or South Asia (figure 1.3). The remaining 14 percent of the global poor, or about 106 million poor people, lived in the other four regions or in high-income economies.7

This is a dramatic shift from 1990, when over half of the poor were living in East Asia and Pacific. The two regions with the most poor people in 1990 were East Asia and Pa-cific and South Asia, which were home to 80 percent of the poor. With China’s rapid re-duction of poverty, the concentration of the global poor shifted from East Asia and Pa-cific in the 1990s to South Asia in 2002, and then to Sub-Saharan Africa in 2010. In South Asia, both the poverty rate and number of poor have been steadily declining, but, given the sheer size of the populations, the con-tribution to global poverty continues to be high. This contrasts with Sub-Saharan Africa, where the total count of poor people in this region has been increasing, essentially lead-

For each region, the average annualized real growth rate between 2005 and 2015 is esti-mated and then used as the growth rate for each country in the region. The third sce-nario is identical to the second but uses twice the historical regional growth averages. These three scenarios all assume that inequality in the country remains unchanged until 2030.

The final scenario explores what happens if growth is pro-poor; if the bottom 40 per-cent on average grows faster than the coun-try as a whole. This scenario, not anchored to any empirical data, assumes that each coun-try grows by 6 percent annually toward 2030, but that the bottom 40 percent, on average, grows by 8 percent annually (while the top 60 percent grows at 4.7 percent, resulting in the average of 6 percent). Because the bottom 40 percent grows at a rate that is 2 percentage points faster than the average, this is referred to as a shared prosperity premium of 2 per-centage points. In all these scenarios, growth rates in either GDP per capita or household final consumption expenditure (HFCE) per capita are rescaled to account for the differ-ence between survey means and national ac-counts as discussed above.5

The scenarios based on growth rates that correspond with historical performance of the countries, or of the average performance of the region do not come close to reaching the target (figure 1.2). Both scenarios suggest global poverty rates in the range of 6 per-cent in 2030. The third scenario, where it is assumed that all countries grow by twice the average regional growth rate over the past ten years, also falls short of the 3 percent target. This scenario predicts a global poverty rate of 3.7 percent in 2030.

This is an alarming finding. The only scenario where the 3 percent tar-

get is met is when a real annual growth rate of 6 percent and a shared prosperity premium of 2 percentage points are assumed.6 The most important element of this scenario is that Sub-Saharan Africa is assumed to grow steadily at this rate for 12 straight years up through 2030. In considering this scenario, it is useful to note that between 2000 and 2015 Sub-Saharan Africa has never had a 10-year average growth rate near 6 percent—let alone 8 percent for the bottom 40. The highest av-erage growth rate was around 2010, when its


ing to the shifting concentration of poverty from South Asia to Sub-Saharan Africa.

This pattern is likely to continue in the coming decade. Simulations show that, as the number of poor continues to decline in South Asia, the forecasts based on historical regional performance indicate that there will be no matching decline in poverty in Sub-Saharan Africa (figure 1.3). In 2030, the share of the global poor residing in Sub-Saharan Africa is forecasted to be about 87 percent, if economic growth over the next 12 years is similar to his-torical growth patterns. (For more details on the simulations, see annex 1B.)

One important reason for the changing regional concentration of poverty, and the projected increase in the share of the global poor residing in Sub-Saharan Africa, is the re-gional differences in per capita GDP growth. Focusing on the three regions that have ac-counted for the bulk of the poor, the average annual growth rate since 1990 has consistently been highest in the East Asia and Pacific re-gion (between 5 and 10 percent), followed by South Asia, and then Sub-Saharan Africa. South Asia has maintained an average growth rate between 5 and 6 percent over the last de-cade (figure 1.4). The average growth rate in

FIGURE 1.3 Number of Poor by Region, 1990–2030

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. World Development Indicators; World Economic Outlook; Global Economic Prospects; Economist Intelligence Unit.

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Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org /PovcalNet/.; World Development Indicators.Note: The orange line reflects the average growth rate as experienced by the population of people in extreme poverty. It is a weighted average of country growth rates where the weights are the number of extreme poor in each country. All curves fit a local polynomial through the annual growth rates to smooth out year-to-year fluctuations.

FIGURE 1.4 Regional GDP per Capita Growth Rates and Average Growth Rate for the Poor, 1990–2017

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ing the global poverty count will occur only if progress is primarily made in those coun-tries where poverty is greatest. This is not to say that countries with extreme poverty rates below 3 percent cannot make further progress. Where there is poverty, there is still much work to be done. But the core indicator the World Bank will track up through 2030 is to reduce the global rate of extreme poverty to less than 3 percent.

If the goal is a world free of poverty, why is progress monitored toward 3 percent and not zero percent? The 3 percent target comes from both empirical and conceptual consid-erations. Empirically, poverty in some coun-tries remains deep, entrenched, and wide-spread; and, when the target was initially set, 3 percent was considered an ambitious but feasible target (Jolliffe et al. 2015). Concep-tually, however, there is also an important reason for setting the target at some level greater than zero percent. The purpose of a target is to assist in efforts to attain goals. For targets to help, they need to be credibly measured and monitored. The key conceptual concern then is that, in general, sample sur-veys from large populations cannot measure rare outcomes well. As countries make prog-ress toward eliminating extreme poverty, the accuracy with which samples can measure the increasingly lower rates deteriorates. At the extreme, sample surveys essentially cannot credibly measure a poverty rate of zero per-cent. In part for this reason, progress is moni-tored toward 3 percent, which can be credibly measured and is also an ambitious goal.

Map 1.1 shows the countries that have pov-erty rates in 2015 of less than 3 percent and highlights the countries that have reached the interim 9 percent target set for 2020. In ad-dition to the 84 countries with poverty rates less than 3 percent, there are 23 countries with poverty rates less than 9 percent. Two-thirds of the countries have rates less than 9 percent. Of the remaining one-third, though, the story is different. In about half of these countries, the poverty rate is greater than 30 percent; and, in 11 countries, the poverty rate is greater than 50 percent. The impressive progress in terms of reducing global poverty to 10 percent globally masks an incredibly high amount of variation of success at the country level in reducing poverty.

Sub-Saharan Africa has rarely exceeded 5 per-cent and has decreased in recent years.

Growth is an important driver of poverty reduction, and, throughout the 1990s and early 2000s, the vast majority of the poor lived in countries with relatively high growth rates. Over the last few years, as the concentration of poverty has shifted to Sub-Saharan Africa, the majority of the poor now live in countries with lower-than-average growth rates (figure 1.4). The orange line in figure 1.4 reflects this change because it is a weighted average of country growth rates where the weights are the number of extreme poor in each coun-try. As the concentration of poor moved from high-growth to low-growth countries, this shift led to a significant deceleration in the rate at which poverty has been declining.

Not only has the growth rate in the coun-tries with the most poor declined in recent years but the conversion of growth to poverty reduction—the growth elasticity of poverty—has also historically been lower in Sub-Saha-ran Africa. Hence, a given growth rate buys less poverty reduction in Sub-Saharan Africa than in most other regions of the world.

The changing regional concentration of poverty reflects the highly uneven rate of pov-erty reduction across countries of the world. Of the 164 countries for which the World Bank monitors poverty, more than half—84 countries—have already reached poverty rates below 3 percent as of 2015. The median poverty rate of the 164 countries in 2015 is 2.7 percent; this median in 2018 is estimated to be 1.9 percent. This success in having more than half the countries of the world with poverty rates below 3 percent is also part of the reason why the world is now starting to experience a slowdown in the rate of poverty reduction. There are now fewer countries than before with large populations of poor people. Pre-viously, progress in poverty reduction could shift over time from one country or region to another, but now there is less scope for this. The slowdown that is observed at the global level does not mean that poverty reduction is declining in every country; however, it does mean that the number of countries where there have been significant declines in the number of poor people is shrinking.

As extreme poverty becomes increasingly concentrated, significant progress in reduc-


the average poverty rate in countries from the East Asia and Pacific region was higher; but, whereas the rates in these countries quickly declined over the years, the decline in the poverty rate in Sub-Saharan Africa was much slower (figure 1.6). Although the percentage of poor in Sub-Saharan Africa has slowly de-clined, this decline has not been fast enough to counter a growing population—the total population of poor people there has steadily increased from 1990 to 2015 (table 1A.1 in annex 1A).8 Economic growth and pro-poor policies in Sub-Saharan Africa over the last 25 years have had anemic benefits in terms of reducing poverty. For simulations that use historical average growth rates as estimates for future growth rates, the predicted future path for the Sub-Saharan Africa rate of pov-erty reduction would continue to be weak and inadequate to bring global poverty to below 3 percent.

Although poverty is comparatively much lower in the Middle East and North Africa, the share of the population in extreme pov-erty increased to 5.0 percent in 2015, up from 2.6 percent in 2013, while the number of poor almost doubled from 9.5 million in 2013 to 18.6 million in 2015. Although these recent

Map 1.1 also marks countries with pov-erty rates between 9 and 18 percent in 2015. This subsample has been created using the simplistic assumption that these countries, if they succeed in reducing poverty by 1 per-centage point a year, will have poverty rates less than 3 percent by 2030. There are 121 countries with poverty rates at or below 18 percent in 2015, and only 43 countries have poverty rates that are higher than this. A closer examination of these countries pro-vides more evidence as to why the 2030 fore-casts indicate that attaining the 3 percent tar-get will be a hard battle.

The map reveals that most of the 43 coun-tries with poverty rates above 18 percent are in Sub-Saharan Africa. Three-fourths of Sub-Saharan African countries had poverty rates above 18 percent in 2015, and, of the world’s 28 poorest countries (that is, those with the highest rates of poverty), 27 are in Sub-Saharan Africa, all with poverty rates above 30 percent (figure 1.5).

In all regions except for Sub-Saharan Af-rica, the regional average rate is well below 18 percent, whereas in Sub-Saharan Africa about 41 percent live below the IPL (figure 1.5). It hasn’t always been like this. In 1990,

MAP 1.1 Poverty Rate by Country, 2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/.

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economic deprivation must be managed ef-ficiently and collectively (World Bank 2013). If not, the risks can turn into economic, en-vironmental and political crises, as in the Middle East and North Africa, where fragility

estimates should be interpreted with caution because of underlying data challenges, they are a stark reminder that past gains cannot be taken for granted. To ensure that progress does not unravel, the risks of falling back into

FIGURE 1.5 Poverty Rate by Region and Country, 2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/.Note: Population-weighted regional average shown in parentheses. Each spike represents a country and all countries within a region are the same color. Within each region, spikes are numbered with the poverty rate if they have the highest rate within the region or if their poverty rate is greater than 50 percent.

a. This estimate is based on a regional population coverage less than 40 percent. The criterion for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year.

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geria) are the five topping the list of countries with the greatest number of extreme poor. India, with over 170 million poor people in 2015, has the highest number of poor people and accounts for nearly a quarter of global

and conflict in the region are impacting live-lihoods and manifesting in the recent spike in poverty.

Drilling down: The countries with the most poor

Over time, many of the countries with high poverty numbers, including Bangla-desh, India, Indonesia, Kenya, and Nigeria, have grown their economies out of low- income-country status and are now middle- income countries. With this growth, most of the extreme poor have also moved from being in low-income to being in middle- income countries, and nearly two-thirds of the world’s poor people now reside in middle-income countries (figure 1.7). How-ever, as more countries shift from low- to middle-income status, so does the popula-tion share. As of 2015, 5.5 billion people lived in middle-income countries as opposed to about 640 million in low-income countries, explaining why most of the extreme poor—over 400 million—now reside in lower- middle-income countries. As countries de-velop and per capita GDP increases, poverty rates tend to fall as economic opportunities are expanded. This general trend can be seen in figure 1.7, with the poverty rate declin-ing from 42 percent for low-income coun-tries to 14 percent for lower-middle-income countries, and close to 2 percent for upper- middle-income countries. This situation is promising for continued poverty reduction if more poor people can benefit from economic growth. Conversely, nearly every low-income country is in Sub-Saharan Africa (and a few countries in other regions, namely Afghani-stan, Haiti, the Democratic People’s Republic of Korea, and Nepal according to the fiscal year 2018), highlighting the need to stimulate and sustain economic growth in low-income countries.

Drilling down a bit further into the coun-tries that have the largest population of poor people, figure 1.8 lists all countries by the share of the global poor in 2015. Half of the people living in extreme poverty in 2015 can be found in just five countries. The most pop-ulous countries in South Asia (Bangladesh and India) and Sub-Saharan Africa (Demo-cratic Republic of Congo, Ethiopia, and Ni-

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FIGURE 1.7 Poverty Rate and Number of Poor, by Income Group, 2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org /PovcalNet/.

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FIGURE 1.6 Poverty Rate, Regional and World Trends, 1990–2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org /PovcalNet/.Note: The regional estimates for Europe and Central Asia in 1990 and South Asia in 1999 and 2015 are based on regional population coverage of less than 40 percent. The criterion for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. Because of the low coverage, these numbers are censored in PovcalNet.


the verge of switching). But the uncertainty about when they have switched or will switch also reflects a series of difficult measurement issues related to global poverty counts. Dis-cussing some of these issues is useful because it can help convey a sense of the level of (im)precision of the poverty counts, and it allows for transparency in the strengths and weaknesses of the data and methods.

In the case of Nigeria, there is one key con-cern with current poverty estimates. Both the 2015 estimate and the 2018 nowcast for Nige-ria are based on household survey data col-lected in 2009. To estimate extreme poverty in 2015 for Nigeria, the survey mean from the 2009 data was increased at a rate equal to the

poverty. In the South Asia region, four out of five extreme poor reside in India. Despite a poverty rate of 13.4 percent, India’s large population of 1.3 billion results in a high ab-solute number of poor. To achieve the global poverty goal, progress in poverty reduction needs to continue in India.

India’s placement as the country with the most poor people in the world is likely to change in the near future. In fact, projections indicate that Nigeria may already have over-taken India. The uncertainty about whether India or Nigeria is currently the country with the most poor people is in part simply be-cause the countries are near a crossing point (having either recently switched or being on

FIGURE 1.8 Distribution of the Global Poor by Region and Country, 2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/.Note: The inner circle is proportionate to the percentage of the total population of poor people living in each region. The outer circle is similarly proportionate, but at the country level. The 10 countries with the most poor people in the world are listed.

East Asia

and Pacific

Indonesia

South Asia

Bangladesh

India

Sub-SaharanAfrica

Mozambique

Kenya

Madagascar

Ethiopia

Tanzania

Congo, Dem. R

ep.

Nige

ria


and how one asks has a significant effect on how people respond (Backiny-Yetna, Steele, and Djima 2017; Beegle et al. 2012; Gibson, Huang, and Rozelle 2003; Jolliffe 2001). Over the years, changes have been introduced in the recall period in the NSS Consumer Ex-penditure Survey, the official instrument for estimating poverty in India. The extreme poverty rate for India as reported here is cur-rently based on an old questionnaire design. With the next NSS data that will be made publicly available, it will no longer be possi-ble to estimate consumption using the same questions and the extreme poverty measure will be estimated using a new questionnaire design. The 2018 nowcast estimates for India indicate that switching from the old to the new questionnaire results in a significantly higher level of total consumption that re-classifies more than 50 million people from poor to not poor. Whenever the next round of NSS data is released (using the new ques-tionnaire), backcasted estimates of poverty in 2015 will most likely show significantly fewer people living in extreme poverty (figure 1.9). For more details on these measurement is-sues for India, see box 1.3.

country’s GDP per capita growth rate (which is estimated annually) and it is assumed that the level of inequality was unchanged over those six years. Similarly, for 2018, the mean is shifted forward on the basis of nine years of growth estimates and assuming unchanged inequality. Although growth measured in surveys used for poverty estimation is cor-related with growth as measured by national accounts data such as GDP, there can be size-able differences and these differences can have substantial impact on estimated poverty rates. Similarly, if the assumption that the distribution (or inequality) has not changed since 2009 is wrong, this too can lead to sub-stantial error in the estimated poverty rate (Jolliffe et al. 2015).

There are two important measurement is-sues that also temper confidence in the India poverty estimates. The first is similar to the issue for Nigeria. The last round of poverty data available was collected in 2011–12. For India, however, an additional round of the National Sample Survey (NSS), collected in 2014–15, has the same socioeconomic and demographic information as the 2011–12, and both provide data on household ex-penditures on services and durables. The 2014–15 NSS also contains three additional schedules with consumption data that were designed to test the potential for changing the questionnaire design, but these data are not in the public domain and were not available for analysis. Given the importance of India to the total poverty count, and the availability of the same socioeconomic, demographic, geographic, and limited consumption data at two points in time, a model of consumption was estimated on the basis of the common variables at these two points in time. The change in the characteristics of the popu-lation of India is leveraged to estimate how much consumption increased over time (in a manner that avoids assuming that inequality did not change). For the cases of both India and Nigeria, the lack of recent data available for analysis results in poverty estimates that are almost certainly much less precise than many other estimates in this report.

The other measurement issue is that there are many different ways to ask survey re-spondents about their consumption habits,

FIGURE 1.9 Poverty Projections for the Five Countries with the Most Poor in 2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. World Development Indicators; World Economic Outlook; Global Economic Prospects. Note: India URP (Uniform Reference Period) relies on poverty estimates and projections based on a uniform recall period; India MMRP (Modified Mixed Reference Period) relies on poverty estimates and projections based on the modified mixed recall period.

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Nigeria is now the country with the most poor people in the world (figure 1.9). When examining a scenario where the consumption measure for India is based on the new ques-tionnaire rather than the old one, the esti-

With the cautions in mind that consump-tion in 2015 for both India and Nigeria is based on projections, not direct enumera-tions of consumption from recent household surveys, the nowcast for 2018 suggests that

BOX 1.3 India: Issues with the 2015 Poverty Estimate and 2030 Forecasts

The 2015 estimate, 2018 nowcast, and 2030 forecasts for India merit special mention given both the importance of India to the global poverty count and the particularly challenging measurement issues. One source of the problem arises from the fact that only a subset of the 2014–15 survey data was released by the government. There are two key issues, the first of which is linked to how survey data from 2011–12 and 2014–15 are used to estimate poverty in India for 2015. The second issue is linked to an impending switch in how India measures consumption, which is the foundation of the poverty estimate.

2015 poverty estimates for India: Imputing consumption

The usual methodology for lining up countries to the reference year (for this report, 2015) is based on two assumptions: the survey mean grows at the same rate as HFCE or GDP per capita, and there is no change in the distribution of consumption. These assumptions may be reasonable when adjusting over a short period of time, but they become problematic as the distance between the survey year and the lineup year increases (Jolliffe et al. 2015).

The latest survey with official poverty estimates for India was conducted in 2011–12, so a 2015 lineup would imply adjusting the survey forward four years. With an HFCE growth rate of 21 percent in India from 2011–12 to 2015, the welfare aggregate for all

households in the 2011–12 survey would be given a growth rate of 21 percent, and poverty in 2015 would be estimated using this adjusted welfare vector. Given India’s importance for the global poverty rate, and the availability of a newer survey (albeit without a full consumption aggregate), it was felt that this extrapolation method needed to be cross-validated.

For this reason, the 2015 poverty estimate for India is based on survey-to-survey imputation method to estimate the growth rate in HFCE. The method uses the 2014–15 National Sample Survey (NSS) that collected consumption information on only a small subset of items but included questions on several correlates of household consumption like household size, age composition of the household, caste status, and labor market indicators. In the first step, a model of the relationship between per capita household consumption and household characteristics is developed using the NSS data from 2004–5, 2009–10, and 2011–12. These surveys have the full consumption questions as well as the variables used in the model. In the second step, the estimated relationship is imposed on the 2014–15 data to predict household consumption and poverty status. See Newhouse and Vyas (2018) for more details on the modeling exercise.

PovcalNet uses the poverty rates at US$1.90 estimated by Newhouse and Vyas (2018) (10.0 percent for urban and 16.8 percent for rural

areas) to calibrate the growth rate in survey mean consumption between 2011–12 and 2014–15. The fraction of growth from national accounts that is passed through to growth in the survey mean implied by this procedure is 55.9 percent for urban India and 73.3 percent for rural India. Earlier projections had used a pass-through of 57 percent (for both urban and rural areas), which was based on the observed historical relationship between the survey and national accounts growth rates (Jolliffe et al. 2015, chapter 1, footnote 14; Ravallion 2003).

The new method used for India marks the first time the World Bank is using inputs from survey-to-survey imputation methods. Thus, there can be a variation in the poverty estimate obtained from the new method and the conventional HFCE-based method. The 2015 extreme poverty rate for India with the imputation-based growth rate is 2.5 percentage points higher than with the HFCE growth rate (13.4 percent versus 10.9 percent).

In the coming years, when countries do not have surveys with full consumption modules, but have other smaller surveys with partial coverage, similar methods may be applied to minimize reliance on the two assumptions implicit in the HFCE approach. Household surveys with full consumption modules are undoubtedly the preferred approach, and only in exceptional cases will the imputation approach be relied upon.

The new imputation approach implies that the poverty estimate

(continued)


Drilling down: Africa and fragile and conflict-affected countries

In 2002, Sub-Saharan Africa was home to less than a quarter of the world’s poor, whereas, in 2015, more extreme poor lived in the region (413 million) than everywhere else in the world combined. If this trend continues

mates indicate that Nigeria overtook India in 2015 as the country with the most poor peo-ple in the world. These projections are based on old surveys and strong assumptions, but, if the historically observed patterns in India and Nigeria continue, Nigeria either already is or soon will be the country with the most poor people in the world.

BOX 1.3 India: Issues with the 2015 Poverty Estimate and 2030 Forecasts (continued)

for India in 2013 needs to also be updated. It has been revised from 16.5 percent to 17.8 percent. The new estimate is based on an average of the estimate from the 2011–12 survey and the 2014–15 survey, where, prior to averaging, the estimates have been lined up to 2013 using the HFCE-based approach described above. This lineup is based on a shorter time period where the two assumptions are less problematic.

An impending change in how consumption is enumerated: Questionnaire design

Recall period affects reported consumption through two main channels: memory decay and telescoping. A longer recall period is better at encompassing expenditure on infrequently purchased items, but it can lead to underreporting if respondents forget about the past purchases. Despite lower average consumption, measured poverty might be lower under the longer recall period because it captures the purchases of low-frequency items of households in the lower parts of the distribution. Short recall periods can mitigate underreporting but can lead to telescoping, where respondents mistakenly report the consumption that took place outside of the reference period.

Until 1993–94, the consumption data in India were collected using

the Uniform Reference Period (URP) method under which questions on household expenditure data for all items were asked for the previous 30-day period. After a series of experiments in the “thin” survey rounds from 1994–95 to 1998, the Mixed Reference Period (MRP) method was introduced in the 1999–2000 survey round in which expenditure on food, pan, and tobacco was collected using 7-day and 30-day recall periods, and the expenditure data for five nonfood items—clothing, footwear, durable goods, education expenses, and institutional medical expenses—were collected using a 365-day recall period (Deaton and Kozel 2005).

With the 2011–12 round of the NSS, the Modified Mixed Reference Period (MMRP) was introduced where the recall period was set at 7 days for perishable items, 365 days for the five low-frequency items, and 30 days for the remaining items (Government of India, Planning Commission 2014). For the sake of comparability over time, the World Bank global poverty count has been based on consumption measures derived from the URP instrument. With the next NSS Consumption and Expenditure Survey, India is no longer enumerating consumption with the URP. This means that the global poverty count produced by the World Bank will soon no longer

be based on the URP for India and a switch to the MMRP will occur.

The choice of method can significantly affect total household consumption and poverty estimates. The official 2004–05 poverty rate for India with the URP-based consumption data was 27.5 percent. The corresponding figure for the MRP-based consumption data was 21.8 percent (Government of India 2007). These changes did not, however, affect the estimates of extreme poverty because the World Bank continued to use the URP-based aggregate for international poverty monitoring to maintain comparability with historical estimates. The poverty estimates and forecasts for India presented here, based on MMRP (figure 1.9), similarly indicate a significant decline in the number of poor people. An important caveat, however, is that the difference in the count of extreme poor as measured by URP and MMRP dissipates with economic growth.

In the most recent “thick” round of the NSS Consumer Expenditure Survey, India has phased out the URP as well as the MRP questions, which means extreme poverty can no longer be tracked using the URP-aggregate. The next update of global poverty will likely show a sizeable drop in the extreme poverty rate and the number of the poor because of India’s switch to the MMRP-based consumption aggregate.


the region, the fast rate of population growth has led to the increase in the total popula-tion of poor people in Sub-Saharan Africa. These demographic features of the region will continue to pose a challenge for poverty reduction, a point that was anticipated by the first World Development Report on poverty (World Bank 1990).

A second contributing factor for the slow decline in poverty in Sub-Saharan Africa is that growth in this region has been less ef-fective in reaching the poor than growth in other regions. One indicator of this is the region’s low growth elasticity of poverty. For every percentage increase in GDP per capita, poverty in a typical non-African developing country falls by 2 percent, whereas in a typ-ical African country it falls by only 0.7 per-cent (Christiaensen, Chuhan-Pole, and Sanoh 2013). There is a caveat to the elasticity com-parison—the level of poverty is much higher in Sub-Saharan Africa so a smaller percentage change in a higher level can still be a signif-icant reduction in poverty—but the general point is that growth in Sub-Saharan Africa has been less effective in reducing poverty than elsewhere. Some of the leading explana-tions for this ineffectiveness of growth in re-ducing poverty include the overall high levels

as the forecasts suggest, extreme poverty will soon become a predominantly African phe-nomenon. An important first step in tackling poverty in the region is to better understand the factors associated with poverty in Sub- Saharan Africa.

One such factor is the demographic struc-ture of the household. In many parts of the world, the poor generally live in larger households and have more economically dependent members per working-age adult (Castaneda et al. 2016). In many regions of the world, the ratio of dependent household members to working-age adults is declining. However, this is not the case in Sub-Saharan Africa. Household surveys from the region show no appreciable decrease in average household size or in the dependency ratio over the 2000s (figure 1.10).

The good news of a declining under-5 mortality rate in Sub-Saharan Africa, and elsewhere in the world (figure 1.11, panel a), has combined with a relatively small drop in the total fertility rate to keep Sub-Saharan Africa’s population growing at a higher rate than that of every other region in the world (figure 1.11, panels b and c) (Canning, Raja, and Yazbeck 2015; Groth and May 2017). Al-though poverty rates have declined slightly in

FIGURE 1.10 Household Size and Dependency Ratio in Sub-Saharan Africa

Source: World Bank Africa Poverty database.Note: The median years for the base period and the terminal period are 2004 and 2011, respectively. Dependency ratio is the ratio of dependents (people younger than 15 or older than 64) to the working-age population (ages 15–64).

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After falling sharply between 2005 and 2011, the poverty rate has since gone up: in 2015, the poverty rate in 35 economies in FCS was 35.9 percent, up from a low of 34.4 percent in 2011. The share of the global poor living in FCS has risen steadily since 2010, culminat-ing in 23 percent of all poor people in 2015 (figure 1.12, panel b).12

This rise has not come about because pop-ulous countries have joined the ranks of frag-ile countries; except for a small drop between 2005 and 2008, the share of the world popu-lation living in fragile settings has stayed level through much of the period (figure 1.12, panel c). Were more countries to become fragile, the goal of rooting out global poverty would

of inequality in several countries and growth that is predominantly in capital-intensive sec-tors like natural resource extraction.

As the global poverty rate declines, there is concern that extreme poverty will become a phenomenon increasingly associated with institutional fragility and conflict. It is also the case that most people (54 percent) liv-ing in fragile and conflict-affected situations (FCS) in 2015 are in Sub-Saharan Africa.9 To see if there is evidence that poverty is al-ready beginning to pool in FCS, trends in the poverty rate and the share of the global poor living in fragile situations are analyzed.10 Figure 1.12, panel a, shows the poverty rate in economies in FCS from 2005 to 2015.11

FIGURE 1.11 Under-5 Mortality Rate, Fertility Rate, and Population Growth Rate in Sub-Saharan Africa

Source: World Development Indicators (http://databank.worldbank.org/data/source/world-development-indicators).

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(World Bank 2017a ).13 For illustration, figure 1.13 plots the performance of countries on a few fundamental indicators of economic and institutional quality against poverty rates. In general, there is a negative correlation be-tween poverty rates and the strength of in-stitutions; countries with high poverty rates have lower financial penetration (panel a; correlation = −0.59), poorer business climate (panel b; correlation = −0.62), weaker rule of law (panel c; correlation = −0.46), and higher perceived corruption (panel d; correlation = −0.43). Notably, fragile countries (marked in red) are often among the poorest performers in these areas, falling in the bottom quintile of the distribution. They must make signifi-

only get more challenging. Panels b and c together also reveal the “poverty burden” borne by the economies in FCS: they have 6.6 percent of the global population but 23 per-cent of the poor, which is 3.5 times higher than would be expected if poverty were equally prevalent everywhere. Despite this significant pooling of poverty, these estimates almost cer-tainly undercount the extent of poverty in FCS for several reasons, including technical mea-surement reasons such as missing data on ref-ugees and displaced persons (see appendix A).

Fragility comprises many elements, and countries that are in fragile situations are characterized by policy failures and institu-tional weaknesses in multiple dimensions

FIGURE 1.12 Concentration of Absolute Poverty in Fragile and Conflict-Affected Situations

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Harmonized List of Fragile Situations (http://www.worldbank.org/en/topic/fragilityconflictviolence/brief/harmonized-list-of-fragile-situations)Note: See appendix A for more details on the list of countries in fragile and conflict-affected situations.

18.9

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Fragile and conflict-affected situationsNot fragile and conflict-affected

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opment programs in proper locations, and target the beneficiary population accurately, it is critically important to know where the poor live, what conditions they live in, and how they earn a living. This description of the poor is frequently done within each country, informing country dialogue on how best to improve the well-being of the less well off in society. But researchers and policy makers can also learn a great deal by examining a global profile of the poor. This examination can aid the international development com-munity to better target poverty alleviation

cant progress on several constituent compo-nents of fragility simultaneously to relieve the constraints to economic growth and poverty reduction.

Socioeconomic and demographic profile of global poverty

To devise an appropriate poverty reduction strategy, it is not enough to merely know how many people are poor. In order to choose the right poverty reduction policies, place devel-

FIGURE 1.13 Fragile Countries Perform Poorly in Multiple Constituent Components of Fragility

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet; The Global Findex Database (https://globalfindex.worldbank.org/); Doing Business (http://www.doingbusiness.org/); and Worldwide Governance Indicators (http://info.worldbank.org/governance/wgi/index.aspx#home). Note: Financial Inclusion Index is the proportion of individuals with a bank account in 2014. Doing Business indicator is the “Distance to Frontier” score for 2014. The 2015 Rule of Law Indicator and the Control of Corruption Indicator are drawn from the Worldwide Governance Indicators (WGI) project. These indicators are used as guideposts to set the World Bank’s CPIA ratings (http://pubdocs.worldbank.org/en/600961531149299007/CPIA-Criteria-2017.pdf).

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A stronger correlation is observed between poverty and educational achievement. Of the adults with no education, more than a fifth are in poverty. There is a premium to having had even some schooling: the poverty rate more than halves for adults with incomplete primary education, whereas poverty is all but absent among adults with some tertiary education. Given that intergenerational mo-bility in education is low in low- and middle- income countries, there is a danger that this pattern will carry over to the next generation as well (Narayan et al. 2018). Increasing labor productivity in agriculture and improving human capital to facilitate labor migration into high-productivity sectors and locations are key to poverty reduction.

The fertility rate is usually higher among the poor. As a result, poor households are usually large and have many children. There are on average 7.7 members and 3.5 children under the age of 14 in the world’s extremely poor households. Just under a fifth of chil-dren under the age of 14 live in poverty, and, despite representing only about a quarter of the population, they make up more than two-fifths of the absolute poor (table 1.1). There is suggestion of increasing concentration of poverty among children, with children under the age of 14 constituting a marginally larger share of the poor in 2015 (45.7 percent) than in 2013 (44.2 percent).16 Children who grow up in poverty acquire less human capital be-cause of inadequate or low-quality schooling and undernutrition. This makes childhood poverty especially pernicious because it per-petuates intergenerational poverty.

The current state of data limits the abil-ity to understand the prevalence of poverty by gender and age. Household surveys collect information on total household consump-tion. They typically do not differentiate how resources are allocated within a household. For analytical purposes, it is assumed that all household members have equal needs and that total consumption is distributed equally within a household. The equal distribution assumption distorts the picture of poverty if there is inequality within households. For example, the profile shows that males and females are equally likely to be in poverty. Chapter 5 takes up this issue in detail and proposes methodological changes in house-

programs as well as areas of well-being re-quiring emphasis.

The profile of the poor is based on har-monized household surveys from 91 coun-tries in the Global Monitoring Database (GMD).14 It is an update of a previous pro-file that was based on the harmonized data for 89 countries for 2013.15 The sample used for this profiling covers about 76 percent of the world’s population and 86 percent of the extreme poor in 2015. The data demands for the global poverty profile are more stringent than that for the global poverty update. It re-quires harmonization of additional variables like age, gender, education, and sector of work from diverse household surveys, which is why the poverty profile is available for only a subset of countries and for an earlier date.

Globally, extreme poverty continues to be disproportionately and overwhelmingly rural. The poverty rate in rural areas (17.2 percent) is more than three times as high as that in urban areas (5.3 percent); with ap-proximately 54 percent of the world’s popu-lation, rural areas account for 79 percent of the total poor. Rural poverty is strongly as-sociated with the sector of employment; the extreme poverty rate is higher among agri-cultural workers, and they constitute almost two-thirds of the extreme poor. But nonfarm employment does not guarantee an escape from poverty; a significant share of poor adults in both urban and rural areas is em-ployed in nonagricultural sectors.

TABLE 1.1 Age and Gender Profile of the Poor, 2015

Poverty rate (%)Share of the

poor (%)Share of the

population (%)Age group0–14 19.3 45.7 27.415–24 11.7 16.9 16.625–34 9.4 13.0 15.935–44 8.7 10.1 13.445–54 6.4 6.4 11.655–64 5.9 4.2 8.265 and up 5.9 3.6 6.9Total 11.5 100.0 100.0

GenderMale 11.7 50.3 49.6Female 11.4 49.7 50.4

Source: Estimates based on the harmonized household surveys in 91 countries, circa 2015, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC.


cline by a percentage point a year the global poverty rate declined by half a percentage point a year, the world would still meet the 3 percent target. Despite this scope for the pace to significantly slacken, all forecasts for 2030 considered in this chapter that are based on countries or regions growing in line with their recent historic performance indicate that the world will fall well short of the target.

Part of the explanation for the deceleration in poverty reduction is that not all regions have shared in the global economic growth of the last quarter century, nor have all re-gions succeeded in ensuring that the poor have fully shared in the benefits of growth. Sub-Saharan Africa has had inadequate levels of growth and inadequate poverty reduction from growth, and this has resulted in the in-crease of the total number of people in this region living in extreme poverty. In 1990, 278 million people in Sub-Saharan Africa lived in extreme poverty; by 2015, this increased to an estimated 413 million people. Forecasts based on historic average growth rates predict that the number of people living in extreme pov-erty in the region will remain above 400 mil-lion in 2030.

A related reason why poverty reduction is slowing is that previously progress rested heavily on the success of the countries of East Asia and Pacific and South Asia in re-ducing the total number of people living in extreme poverty. The countries of East Asia and Pacific have experienced remarkable reductions in extreme poverty. In 1990, there were 987 million people living in ex-treme poverty in this region, and this num-

hold surveys to capture the intrahousehold distribution of consumption. In the mean-time, differences in poverty by gender and age will be informed by assuming someone is poor if he or she lives in a poor household.

The poor lack not just income. Poverty also materializes as low educational attain-ment, poor health and nutrition outcomes, exposure to physical insecurity and natu-ral hazards, and substandard living condi-tions. Globally, a large share of extreme poor households has no adult member with pri-mary schooling, and in many households at least one child of school age (up to grade 8) is out of school (table 1.2). The poor are also poorly served in essential services like accept-able standards of drinking water, adequate sanitation facilities, and electricity (table 1.2).

Low levels of human capital and poor access to basic services undermine labor productivity of the poor, often their most important source of income, trapping them in income poverty. Increasingly, however, poverty is understood as encompassing more than just income. Sufficient education, good health, a safe living environment, and pro-vision of basic services are desired for their intrinsic value, beyond their instrumental value in raising income. Chapter 4 takes a panoramic view of poverty as the inability to reach a sufficiency threshold in monetary terms as well as in a wide range of nonmon-etary dimensions that directly affect an indi-vidual’s well-being.

ConclusionsBetween 1990 and 2015, the world made steady progress toward the target of reduc-ing the number of people living in extreme poverty to less than 3 percent globally by 2030. The extreme poverty rate dropped on average 1 percentage point every year, falling from 35.9 percent in 1990 to 10.0 percent in 2015. As a result of this decline, there were well over a billion fewer people living in pov-erty despite a global population that had in-creased by more than 2 billion people during this period. With the estimated extreme pov-erty rate at 10 percent in 2015, the target of 3 percent by 2030 could be attained even if the rate of poverty reduction was cut in half. That is to say, if instead of continuing to de-

TABLE 1.2 Education and Access to Services Among Poor and Nonpoor Households

Share of households (%)Poor Nonpoor

No adult member has completed primary education 53.1 12.2At least one school-age child (up to grade 8) is out of school 22.8 3.4Household does not have access to limited-standard source of drinking water

37.0 8.6

Household does not have access to limited-standard sanitation facilities

66.8 16.3

Household does not have access to electricity 67.8 7.1

Source: Estimates based on the harmonized household surveys in 119 countries, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC.


erage poverty rates in all regions of the world except for Africa are below 2 percent; how-ever, the forecasted average extreme poverty rate for Sub-Saharan Africa is above 25 per-cent. Even in a forecast based on an assumed real growth rate of 8 percent, the 3 percent global target is met but extreme poverty in Sub-Saharan Africa is in double digits (13.4 percent).

This sort of outcome, where extreme pov-erty is eliminated throughout the world ex-cept in one region where it is in double dig-its certainly does not portray a picture of a world free of poverty. This, then, is one of the key messages of this report: it is time to go beyond the focus on bringing down the aver-age global poverty rate to 3 percent to reach the goals of eradicating extreme poverty and ensure that all share in the benefits of eco-nomic development.

A key point of this report is that the view of poverty needs to be broadened. Now that the extreme poverty rate is less than 3 percent in half the countries of the world and is be-coming increasingly concentrated, finishing the job will require constructing a more de-tailed and complete picture of what is meant by a world free of poverty. To do this, the next chapters in this report go beyond extreme income poverty to start the process of moni-toring poverty in all its forms. New measures introduced in this report allow one to better monitor poverty in all countries, in multiple aspects of life, and for all individuals in every household.

ber dropped to 47 million people by 2015. On average, each year ended with about 38 million fewer people living in extreme pov-erty in the East Asia and Pacific region. But now, with the prevalence of extreme poverty below 3 percent, and the number of poor in this region contributing only about 6 percent to the total population of poor, there are few remaining gains to be made in this region in terms of having a significant effect in reduc-ing the global poverty rate.

Although there are still many extreme poor in South Asia, a similar story will most likely soon occur there, and this is good news. In 1990, more than a half billion people in South Asia lived in extreme poverty; by 2015, this dropped to 216 million people. A rela-tively large portion of the extreme poor still live in South Asia, but the forecasts indicate (combined with the anticipated change in how consumption is measured in India) that the total number of poor there is rapidly de-clining. The success in reducing extreme pov-erty in many regions of the world means that the majority of the remaining gains in pov-erty reduction must come from the countries of Sub-Saharan Africa.

The unevenness of the progress in global poverty reduction brings into focus the rel-ative strengths and weaknesses in how prog-ress toward the goal of a world free of poverty is monitored. In various forecasts assuming that countries continue to grow in line with their recent performance (or with the aver-age historic growth rate of their region), av-


This annex contains tables of historical pov-erty rates at the global, regional, and country levels. Poverty rates do not speak to the dis-tribution of poverty among the poor, mean-ing that the poor may fare worse in certain countries than in others. For this reason, the poverty rates are complemented with other measures of poverty: the poverty gap, the poverty gap divided by the poverty rate, and the squared poverty gap (Foster, Greer, and Thorbecke 1984).

The poverty gap measures the average dis-tance to the poverty line, where people above the poverty line are given a distance of zero. This measure reflects both the share of poor and the average daily consumption of the poor, but expressed as the average shortfall among the entire population. If two countries have the same poverty rate, but the poor in the first country have a daily consumption of US$1.50, whereas in the other they have a daily consumption of US$1.80, then the pov-erty gap will indicate a higher depth of poverty in the first country. When the poverty gap is divided by the poverty rate, the resulting num-ber shows the average distance to the poverty line, or average consumption shortfall among the poor. If the average consumption shortfall of the poor is 0.25, then poor individuals on average consume 25 percent less than the value of the IPL, or US$1.43 per day ( (1-0.25)*IPL ).

Since 1990, both of these complementary measures of poverty have improved. The total consumption gap of the poor (the sum of all consumption shortfalls of the poor) shrank from US$1,276 million (2011 PPP) in 1990 to US$433 million (2011 PPP) in 2015 (figure 1A.1). This improvement reflects both that the share of people living in extreme poverty has decreased and that the average income of the poor has increased over this time interval.

Although both the poverty gap and the poverty gap divided by the poverty rate are sensitive to the average level of poverty among the poor, they do not account for in-equality among the poor. The squared pov-erty gap—which is the average squared dis-tance to the poverty line, where people above the poverty line have a distance of zero—is sensitive to inequality among the poor. Sup-pose that two countries have the same pov-erty rate, and the poor in both countries on average consume US$1.50 daily. Suppose further that, in one of the countries, all the poor consume US$1.50, whereas the other country has many people consuming much less. The squared poverty gap measures this latter country, with greater inequality among the poor, as having a higher level of poverty.

An issue to keep in mind with these com-plementary poverty measures is that they are more sensitive to whether poverty is mea-sured with consumption or income. Whereas

Annex 1A

Historical global and regional poverty estimates

FIGURE 1A.1 Global Total Consumption Gap of the Poor, 1990–2015

Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/.Note: PPP = purchasing power parity.

1276

433

0

200

400

600

800

1,000

1,200

1,400

1990 1995 2000 2005 2010 2015

Mill

ion

of U

S$ (2

011

PPP)


poverty estimates based on income can be zero—and even negative—in a given period because of negative income shocks, they rarely get close to zero when consumption is used. This makes it more likely that a country

using income is faring poorly in these com-plementary measures, and it makes it difficult to compare the depth of poverty across coun-tries using consumption and income (see ap-pendix A for more discussion on this).

TABLE 1A.1 Historical Global and Regional Extreme Poverty Trends, 1990–2015

a. World extreme poverty estimates, 1990–2015

Year Poverty rate (%) Poverty gap (%)Squared

poverty gap Poor (millions) Population (millions)

1990 35.9 12.7 6.1 1,894.8 5,284.91993 33.9 11.9 5.8 1,877.5 5,542.91996 29.4 9.8 4.7 1,703.2 5,792.61999 28.6 9.5 4.5 1,728.6 6,038.12002 25.6 8.3 3.9 1,609.9 6,276.82005 20.7 6.3 2.9 1,352.2 6,517.02008 18.1 5.4 2.4 1,223.2 6,763.72011 13.7 4.1 1.9 963.0 7,012.82013 11.2 3.4 1.6 804.2 7,182.92015 10.0 3.1 1.5 735.9 7,355.2

b. Regional poverty rates, 1990–2015

Percent

Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015

East Asia and Pacific 61.6 54.0 41.1 38.8 29.9 19.1 15.1 8.6 3.6 2.3Europe and Central Asia 2.9 5.0 7.2 7.8 5.9 4.9 2.8 2.1 1.6 1.5Latin America and the Caribbean 14.2 13.2 13.8 13.6 11.8 9.9 6.9 5.6 4.6 4.1Middle East and North Africa 6.2 6.7 5.8 3.8 3.2 3.0 2.7 2.7 2.6 5.0South Asia 47.3 44.9 40.3 39.3 38.6 33.7 29.5 19.8 16.2 12.4Sub-Saharan Africa 54.3 58.9 58.2 57.7 56.4 50.7 47.8 45.1 42.5 41.1

Sum of regions 43.1 40.6 35.1 34.0 30.4 24.5 21.3 16.1 13.1 11.6Rest of the world 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.7

World 35.9 33.9 29.4 28.6 25.6 20.7 18.1 13.7 11.2 10.0

c. Regional number of extreme poor, 1990–2015

Millions

Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015

East Asia and Pacific 987.1 902.0 712.9 695.9 552.5 361.6 292.8 169.6 73.1 47.2Europe and Central Asia 13.3 23.4 33.8 36.7 27.6 22.9 13.3 9.8 7.7 7.1Latin America and the Caribbean 62.6 61.3 67.7 69.7 63.2 54.9 39.9 33.8 28.0 25.9Middle East and North Africa 14.2 16.6 15.3 10.6 9.4 9.4 8.8 9.2 9.5 18.6South Asia 535.9 542.1 518.0 534.4 554.3 510.4 467.0 328.0 274.5 216.4Sub-Saharan Africa 277.5 327.3 350.7 376.1 398.0 387.7 396.4 406.4 405.1 413.3

Sum of regions 1,890.5 1,872.7 1,698.3 1,723.5 1,605.0 1,346.9 1,218.1 956.9 797.8 728.5Rest of the world 4.3 4.9 4.9 5.0 4.9 5.3 5.1 6.2 6.4 7.3

World 1,894.8 1,877.5 1,703.2 1,728.6 1,609.9 1,352.2 1,223.2 963.0 804.2 735.9

Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: Sum of regions was previously referred to as developing world. a. This estimate is based on a regional population coverage of less than 40 percent. The criterion for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year.

a

a a

a

a a



TABLE 1A.2 Poverty Estimates, by Economy, 2015

EconomySurvey year(s) of the

poverty estimateNumber of poor

(millions)Pov. rate

(%)Pov. gap

(%)Pov. gap/Pov. rate

(%)

Albania 2012 0.0 0.9 0.2 20.0Algeria 2011.17 0.1 0.4 0.1 37.1Angola 2008.5 7.8 27.9 8.7 31.2Argentina 2014 and 2016 0.3 0.6 0.3 45.3Armenia 2015 0.1 1.9 0.4 18.8Australia 2010 0.1 0.5 0.3 62.0Austria 2015 0.1 0.8 0.5 72.0Azerbaijan 2005 0.0 0.0 0.0Bangladesh 2010 and 2016 24.4 15.2 2.8 18.1Belarus 2015 0.0 0.0 0.0Belgium 2015 0.0 0.0 0.0Belize 1999 0.0 13.9 6.0 43.1Benin 2015 5.2 49.6 22.4 45.1Bhutan 2012 and 2017 0.0 1.7 0.3 16.3Bolivia 2015 0.7 6.4 2.8 44.3Bosnia and Herzegovina 2015 0.0 0.2 0.1 30.0Botswana 2009.25 0.3 12.8 3.7 29.3Brazil 2015 6.9 3.4 1.2 34.5Bulgaria 2014 0.1 1.2 0.5 36.3Burkina Faso 2014 7.8 42.8 10.8 25.2Burundi 2013.5 7.6 74.7 32.9 44.0Cabo Verde 2007.33 0.0 7.2 1.7 23.0Cameroon 2014 5.2 22.8 7.1 31.3Canada 2013 0.2 0.5 0.2 32.0Central African Republic 2008 3.5 77.7 44.0 56.6Chad 2011 4.8 34.1 13.2 38.7Chile 2015 0.2 1.3 0.8 58.5China 2015 10.0 0.7 0.2 21.9Colombia 2015 2.2 4.5 1.7 38.2Comoros 2013.5 0.1 18.2 6.5 35.7Congo, Dem. Rep. 2012.4 55.1 72.3 34.6 47.9Congo, Rep. 2011 1.7 34.9 13.5 38.7Costa Rica 2015 0.1 1.5 0.6 38.8Côte d’Ivoire 2015 6.5 28.2 9.1 32.4Croatia 2015 0.0 0.8 0.4 46.7Cyprus 2015 0.0 0.0 0.0Czech Republic 2015 0.0 0.0 0.0Denmark 2015 0.0 0.2 0.1 57.1Djibouti 2013 0.2 18.6 6.3 33.9Dominican Republic 2015 0.2 1.9 0.5 25.5Ecuador 2015 0.6 3.4 1.2 35.8Egypt, Arab Rep. 2015 1.3 1.4 0.2 11.9El Salvador 2015 0.1 1.9 0.4 20.7Estonia 2015 0.0 0.5 0.4 78.7Eswatini 2009.25 0.5 39.0 14.8 37.9Ethiopia 2010.5 and 2015.5 27.0 27.0 7.7 28.6Fiji 2013.24 0.0 1.0 0.2 16.7Finland 2015 0.0 0.0 0.0France 2015 0.0 0.0 0.0Gabon 2005 and 2017 0.1 4.1 1.0 24.1Gambia, The 2010.08 and 2015.31 0.2 11.1 2.5 22.9Georgia 2015 0.1 4.0 1.0 24.7Germany 2015 0.0 0.0 0.0Ghana 2012.8 3.0 10.9 3.1 28.7Greece 2015 0.2 1.5 0.8 52.7Guatemala 2014 1.3 7.9 2.3 29.3Guinea 2012 4.0 33.0 9.4 28.4Guinea-Bissau 2010 1.2 65.3 29.4 44.9

(continued)


TABLE 1A.2 Poverty Estimates, by Economy, 2015 (continued)



(millions)Pov. rate

(%)Pov. gap


(%)

Guyana 1998 0.1 6.5 1.9 28.9Haiti 2012 2.5 23.7 7.6 32.1Honduras 2015 1.4 16.2 5.6 34.9Hungary 2015 0.0 0.5 0.3 61.2Iceland 2014 0.0 0.0 0.0India* 2011.5 175.7 13.4 2.4 17.7Indonesia 2015 18.5 7.2 1.2 16.6Iran, Islamic Rep. 2014 0.3 0.4 0.1 16.2Iraq 2012 0.8 2.2 0.3 14.8Ireland 2015 0.0 0.2 0.2 95.7Israel 2012 0.0 0.5 0.3 54.2Italy 2015 1.2 2.0 1.4 70.5Jamaica 2004 0.1 1.8 0.4 22.8Japan 2008 0.3 0.2 0.2 68.2Jordan 2010.24 0.0 0.2 0.0 16.7Kazakhstan 2015 0.0 0.0 0.0Kenya 2005.38 and 2015.67 17.6 37.3 11.9 31.9Kiribati 2006 0.0 11.8 3.0 25.4Korea, Rep. 2012 0.1 0.3 0.1 44.0Kosovo 2015 0.0 0.4 0.1 20.0Kyrgyz Republic 2015 0.2 2.5 0.5 18.5Lao PDR 2012.25 0.9 14.0 2.9 20.7Latvia 2015 0.0 0.7 0.4 47.3Lebanon 2011.77 0.0 0.0 0.0Lesotho 2010 1.2 54.8 28.1 51.3Liberia 2014 1.8 40.2 12.3 30.7Lithuania 2015 0.0 0.8 0.5 72.0Luxembourg 2015 0.0 0.2 0.2 95.0Macedonia, FYR 2014 0.1 5.0 2.4 47.2Madagascar 2012 18.8 77.5 38.8 50.1Malawi 2010.23 12.2 69.6 31.7 45.6Malaysia 2013 and 2015.33 0.0 0.0 0.0Maldives 2009.5 0.0 4.1 0.8 20.3Mali 2009.89 8.3 47.8 14.5 30.4Malta 2015 0.0 0.0 0.0Mauritania 2014 0.3 6.2 1.5 23.9Mauritius 2012 0.0 0.4 0.1 17.5Mexico 2014 and 2016 4.2 3.3 0.8 24.4Micronesia, Fed. Sts. 2013 0.0 15.4 5.5 35.9Moldova 2015 0.0 0.0 0.0Mongolia 2014 and 2016 0.0 0.2 0.0 10.0Montenegro 2014 0.0 0.0 0.0Morocco 2013.5 0.3 0.9 0.2 17.4Mozambique 2014.44 17.4 62.2 27.3 43.8Myanmar 2015 3.3 6.4 1.5 23.1Namibia 2009.54 and 2015.27 0.3 13.4 4.5 33.8Nepal 2010.17 2.0 7.0 1.4 19.8Netherlands 2015 0.0 0.0 0.0Nicaragua 2014 0.2 2.9 0.6 22.3Niger 2014 8.9 44.5 13.5 30.2Nigeria 2009.83 86.5 47.8 18.6 38.9Norway 2015 0.0 0.2 0.0 16.7Pakistan 2013.5 and 2015.5 9.9 5.2 0.7 13.2Panama 2015 0.1 2.0 0.5 26.8Papua New Guinea 2009.67 2.3 28.4 10.3 36.3Paraguay 2015 0.1 1.9 0.4 21.7Peru 2015 1.1 3.6 1.0 27.3

(continued)




(millions)Pov. rate

(%)Pov. gap


(%)

Philippines 2015 8.5 8.3 1.6 18.9Poland 2015 0.0 0.0 0.0Portugal 2015 0.1 0.5 0.3 50.0Romania 2015 1.1 5.7 1.9 33.4Russian Federation 2015 0.0 0.0 0.0Rwanda 2013.75 6.0 51.5 17.6 34.2Samoa 2008 0.0 1.1 0.1 10.5São Tomé and Príncipe 2010 0.1 26.0 6.2 24.0Senegal 2011.29 5.3 35.7 11.4 31.9Serbia 2015 0.0 0.1 0.0 30.0Seychelles 2013 0.0 1.0 0.4 40.6Sierra Leone 2011 3.5 48.4 14.8 30.5Slovak Republic 2015 0.0 0.7 0.3 35.1Slovenia 2015 0.0 0.0 0.0Solomon Islands 2013 0.1 24.7 6.7 26.9South Africa 2014.83 10.4 18.9 6.2 32.8South Sudan 2009 8.7 73.3 40.0 54.6Spain 2015 0.5 1.0 0.6 64.6Sri Lanka 2012.5 and 2016 0.2 0.8 0.1 11.7St. Lucia 1995 0.1 28.3 9.8 34.6Sudan 2009 3.0 7.7 2.0 25.8Suriname 1999 0.1 18.8 14.5 77.0Sweden 2015 0.0 0.5 0.3 50.0Switzerland 2015 0.0 0.0 0.0Syrian Arab Republic 2004 4.0 21.2 4.8 22.4Tajikistan 2015 0.4 4.8 1.1 21.8Tanzania 2011.77 21.9 40.7 11.7 28.9Thailand 2015 0.0 0.0 0.0 33.3Timor-Leste 2014 0.4 31.2 6.9 22.0Togo 2015 3.6 49.2 19.9 40.5Tonga 2009 0.0 1.0 0.2 21.4Trinidad and Tobago 1992 0.0 0.6 0.2 35.7Tunisia 2010.41 0.1 0.9 0.2 18.3Turkey 2015 0.2 0.3 0.1 21.4Turkmenistan 1998 0.2 2.8 0.4 15.5Tuvalu 2010 0.0 2.4 0.2 6.8Uganda 2012.45 and 2016.5 15.8 39.2 12.3 31.2Ukraine 2015 0.1 0.1 0.0 8.3United Kingdom 2015 0.1 0.2 0.1 39.1United States 2013 and 2016 3.7 1.2 1.0 82.8Uruguay 2015 0.0 0.1 0.0 23.1Uzbekistan 2003 4.4 14.0 3.8 26.8Vanuatu 2010 0.0 12.8 3.2 24.7Venezuela, RB 2006 2.8 8.9 6.6 74.7Vietnam 2014 and 2016 2.1 2.3 0.4 19.1West Bank and Gaza 2011 and 2016.75 0.0 0.6 0.1 15.3Yemen, Rep. 2014 11.0 40.9 12.0 29.3Zambia 2015 9.3 57.5 29.5 51.3Zimbabwe 2011 2.5 16.0 3.5 21.5

TABLE 1A.2 Poverty Estimates, by Economy, 2015 (continued)

Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: The year column refers to the year of the survey that is used to calculate the 2015 estimate as listed in PovcalNet. Note that for economies that use EU-SILC surveys, the survey year is 2016, but the year listed is 2015 because the 2016 survey contains income data for 2015. If one year is listed, and this year is different from 2015, the poverty estimate from the year of the survey has been extrapolated to 2015. If two years are listed, the 2015 estimates are based on interpolations between these two surveys. For more information on how these interpolations and extrapolations are carried out, see appendix A. The decimal year notation is used when data are collected over two calendar years. The number before the decimal point refers to the first year of data collection, while the numbers after the decimal point show the proportion of data collected in the second year. For example, the Algerian survey (2011.17) was conducted in 2011 and 2012, with 17 percent of the data collected in 2012. Pov. rate is the poverty rate, or the percentage of the population living on less than the IPL (international poverty line). Pov. gap is the average consumption shortfall of the population where the nonpoor have no shortfall (as described above). Pov. gap / pov. rate is the average consumption shortfall of the poor (as described above). * indicates that the 2015 estimate for India is based on an imputation described in box 1.3.


the actual poverty levels in 2015. To match the actual global poverty rate observed in 2015, the 2002–15 projections would need to use annual growth rates in the range of 6 per-cent per year.

Although this example speaks to the in-herent uncertainty of making long-term pov-erty projections, even with an annual growth rate across the globe of 6 percent until 2030, the projections still do not predict that the 3 percent target will be met.

The poverty projections to 2030 are based on several critical assumptions regarding countries’ future growth rates and the nature of this growth. The global poverty patterns in 2030 may look very different if these as-sumptions are not met. The soundness of the 2030 forecasts can be assessed indirectly by pretending that the poverty levels and growth rates from 2002–15 are unknown and apply-ing the forecast methodology to 2002–15. For example, one can use the country-specific and regional growth rates from 1992 to 2002 to predict poverty rates from 2002 to 2015. With this approach, the 2015 forecasts can be benchmarked against the realized pov-erty levels in 2015. This would help uncover the sensitivity of the assumptions behind the projections and hence give an indica-tion of the uncertainty surrounding the 2030 projections.

Using this approach, the global rate of extreme poverty is predicted to be 13.4 per-cent in 2015—well above the actual rate of 10.0 percent (figure 1B.1). This is largely because the regional growth rates in Sub- Saharan Africa are severely underestimated using historical growth data.17 The regional growth rate in GDP per capita in Sub- Saharan Africa from 1992 to 2002 was 0.7 percent, whereas the actual growth in GDP per capita from 2002 to 2015 turned out to be several percentage points higher. Hence, the historical growth rates were not a good indication of the future growth rates, and the projections overestimate the amount of pov-erty in Sub-Saharan Africa in 2015. In other regions, such as East Asia and Pacific and South Asia, the projections are very close to

Annex 1B

Validation check of the 2030 poverty projections

FIGURE 1B.1 Global Poverty Projections, 2002–15

Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank.Note: The figure assumes 2002 is the latest year of data and applies the forecasting methods used toward 2030 to obtain pov-erty “forecasts” for 2002–15. This can be benchmarked against realized poverty levels, and hence allows for an assessment of the soundness of the 2030 projections.

5

10

15

20

25

30

35

1990 1995 2000 2005 2010 2015

Pove

rty

rate

(%)

Projection using regional growth rates, 1992–2002Projection using country growth rates, 1992–2002Projection using 6% annual growth rateActual trend


0.544 for the Middle East and North Africa,

0.912 for South Asia, 0.748 for Sub-Saharan

Africa, and 0.892 for the rest of the world.

5. GDP rates are used for Sub-Saharan Africa

and for countries without HFCE growth rates.

The same pass-through rates are applied as in

the nowcast. The average regional growth rate

is weighted using each country’s population in

2015 as the weight.

6. Projections based on a global growth rate of

8 percent and no shared prosperity premium

are nearly identical to the 6 percent growth

and 2 percentage point premium scenario,

and thus also get the global rate below 3 per-

cent by 2030. In general, a mean growth rate

of x percent combined with a shared pros-

perity premium of y percent is nearly identi-

cal to a growth rate of x + y percent and no

shared prosperity premium. Projections using

a 7 percent global growth rate and no shared

prosperity premium, or a 5 percent growth

rate and a 2-percentage-point premium, get

very close to the 3 percent target.

7. East Asia and Pacific (6.4 percent), Latin

America and the Caribbean (3.5 percent), the

Middle East and North Africa (2.5 percent),

Europe and Central Asia (1.0 percent), and

the rest of the world (1.0 percent).

8. Some evidence suggests that, if price differ-

ences within countries are accounted for, the

reduction in Sub-Saharan Africa has been

greater than the numbers suggested here

(Beegle et al. 2016). For more information on

the impact of price differences within coun-

tries on poverty, see appendix A.

9. Of the 35 economies in FCS in 2015, 16 (45.7

percent) were in Sub-Saharan Africa. In terms

of population, of the 481.1 million people liv-

ing in FCS, 259.8 million (54.0 percent) were

in Sub-Saharan Africa. More details on how

countries are determined to be in FCS are

given in appendix A.

10. The analysis uses a “rolling” roster of fragile

countries, that is, the set of fragile countries

can change from one year of the analysis to

the next.

11. This analysis goes back only to 2005 because

the World Bank classification of fragile coun-

tries began that year.

12. The aggregate FCS poverty rate is the

population-weighted mean of the poverty

Notes 1. The interim target of a poverty rate of 9 per-

cent was set by the World Bank Group presi-

dent at the 2013 Annual Meetings: http://www

.world bank.org/en/news/speech/2013/10/11

/world-bank-group-president-jim-yong-kim

-speech-annual-meetings-plenary.

2. Survey coverage is assessed by considering

surveys within a two-year window on either

side of 2015, that is, surveys conducted be-

tween 2013 and 2017. By this criterion, two-

thirds of the world is covered by a survey for

the 2015 poverty update. The coverage would

be lower for more recent years.

3. The core poverty numbers reported in this

chapter are for 2015. These numbers are re-

ferred to as estimates. References to a nowcast

indicate that the poverty rate is a forecasted

estimate up to the current point in time,

which for this report is 2018. Because this re-

lies largely on realized growth rates and pop-

ulation figures, it should, in principle, be more

reliable than a forecast. References to forecasts

are used when the prediction is more remote

in the future, later than the nowcast, typically

2030. Forecasts are based on assumed growth

rates and predictions of population figures and

are estimated with significantly less precision.

4. The growth rates used are from the World

Development Indicators. Pass-through rates

are essentially estimated by comparing av-

erage differences between national accounts

and household surveys. The mean national

consumption or income from each country’s

household survey is compared with either

GDP or household final consumption expen-

diture (HFCE) from national accounts. HFCE

is the preferred measure in most countries,

except in Sub-Saharan Africa where GDP is

used for estimating pass-through factors and

growth rates. If GDP and HFCE data are un-

available, growth forecasts from the Global

Economic Prospects are used. If these are also

unavailable, growth forecasts from the World

Economic Outlook are used. For Syria, no es-

timates are available in these sources. Instead,

data from the Economist’s Intelligence Unit are

relied upon. The fraction of GDP/HFCE per

capita that is assumed to pass through to the

welfare vector is as follows: 0.785 for East Asia

and Pacific, 0.773 for Europe and Central Asia,

0.829 for Latin America and the Caribbean,


15. The 2013 profile and the methodological de-

tails are reported in Castaneda et al. (2016).

16. The 2013 estimates are based on a different set

of 89 countries (Castaneda et al. 2016). When

the 2013 profiling is repeated using the same

91 countries from 2015, children constitute

44.9 percent of the poor.

17. Another reason for the discrepancy is that the

projections used here assume that only a frac-

tion of the growth rates observed in national

accounts translates into growth in the con-

sumption aggregate observed in surveys. The

actual poverty numbers, in contrast, assume

that all growth observed in national accounts

translates into growth in the consumption ag-

gregate. This implies that the projections are

more pessimistic than the actual estimates for

countries where the 2015 estimate is based on

extrapolation.

rates of all economies in FCS. The number of

poor is a product of the FCS poverty rate and

the total population living in FCS. This leads

to a slightly higher estimate of the total num-

ber of poor (744 million versus PovcalNet es-

timate of 736 million), but the discrepancy is

inconsequential to the current discussion.

13. The World Bank’s definition of fragility is

based on the Country Policy and Institu-

tional Assessment (CPIA), which assesses the

conduciveness of a country’s policies and in-

stitutions to poverty reduction, sustainable

growth, and the effective use of development

assistance. The CPIA comprises 16 indicators

clustered in four dimensions: economic man-

agement, structural policies, policies for social

inclusion and equity, and public sector man-

agement and institutions.

14. Please refer to appendix A for more informa-

tion on the GMD.

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